\name{gamlss.ts-package}
\alias{gamlss.ts-package}
\alias{gamlss.ts}
\docType{package}
\title{
Time Series and GAMLSS
}
\description{
This package will contain several function suited to fit time series model. 
At the moment only contains \code{garmaFit()} to fit a GARMA model, see Benjamin et al. (2003). Other function will appear soon. The idea is to be able to use 
techniques design for normally distributed time series data to all the \code{gamlss.family} distributions.   
}
\details{
\tabular{ll}{
Package: \tab gamlss.ts\cr
Type: \tab Package\cr
Version: \tab 1.0\cr
Date: \tab 2011-04-28\cr
License: \\tab GPL (version 2 or later)\cr
LazyLoad: \tab yes\cr
}
~~ An overview of how to use the package, including the most important functions ~~
}
\author{
Mikis Stasinopoulos <\email{d.stasinopoulos@londonmet.ac.uk}>, Bob Rigby <\email{r.rigby@londonmet.ac.uk}> 
Vlasis Voudouris

Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>

}
\references{

Benjamin M. A., Rigby R. A. and Stasinopoulos D.M. (2003) Generalised Autoregressive Moving  Average Models.  \emph{J. Am. Statist. Ass.}, 98, 214-223.

Rigby, R. A. and  Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), 
\emph{Appl. Statist.}, \bold{54}, part 3, pp 507-554.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R.
Accompanying documentation in the current GAMLSS  help files, (see also  \url{http://www.gamlss.com/}).

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
\emph{Journal of Statistical Software}, Vol. \bold{23}, Issue 7, Dec 2007, \url{http://www.jstatsoft.org/v23/i07}.

}

\keyword{ package }

\examples{
data(polio)
ti <- as.numeric(time(polio))
mo <- as.factor(cycle(polio))
x1 <- 0:167    #Index used in Tutz p197
x2 <- cos(2*pi*x1/12)
x3 <- sin(2*pi*x1/12)
x4 <- cos(2*pi*x1/6)
x5 <- sin(2*pi*x1/6)
# all the data here 
da <-data.frame(polio,x1,x2,x3,x4,x5, ti, mo)
m02 <- garmaFit(polio~x1+x2+x3+x4+x5, order=c(0,2), data=da, family=NBI, tail=3)
summary(m02)
}
